With the advancement of technologies and increased demand, video surveillance technologies have been rapidly developed and widely used in a variety of areas, such as smart surveillance and transportation. Video surveillance refers to any technology that utilizes cameras or other imaging sensors to capture one or more images of an environment, and performs image analysis to identify objects and/or obtain information for various purposes. Detection and tracking of moving object is an important subject of computer vision research. Technical advancement and implementations of moving object detection and tracking have attracted a lot of attention in the society.
Traditional techniques for detecting a moving object include: optical flow-based methods, frame difference-based methods, background difference- or subtraction-based methods, methods based on color tracking (e.g., meanshift), and object detecting and tracking methods based on pattern recognition.
Optical flow-based methods use characteristics of optical flows (e.g., pixel movement vectors derived from images captured overtime) to detect and track moving objects. However, optical flow-based methods require complex computation. In addition, optical flow-based methods are sensitive to signal noise. Such methods require high computational capacity in order to realize real time detection and tracking of moving objects.
Frame difference-based methods detect areas of movement in two or three image frames that are consecutively obtained over time, based on difference methods. Such methods are relatively simple and insensitive to changes in light in the scenes. However, it is easy to have void space in the detected moving object. As a result, the detected area of movement tends to be incomplete.
Background difference or subtraction methods have been frequently used in motion detection. Such methods calculate the difference between a present image frame and a background image (or image frame) to obtain an area of movement. Such methods generally produce a complete area of movement. However, such methods may be sensitive to changes in light and scene, and may exhibit noise in the result, which may limit the effectiveness of such methods.
Examples of methods based on color tracking include meanshift and camshift methods. Camshift methods use histogram model of colors of an object to obtain a back projection model, thereby converting image data to a probability distribution plot of colors, and enabling tracking based on colors. Meanshift methods are non-parametric methods based on density function gradient. Such methods locate an object in the image data by finding the extreme values of a probability distribution through iteration. These methods use the color of an object as the primary information for tracking, and are fast and effective for tracking a single-color moving object. However, these methods are sensitive to color. For example, when the background includes objects that have colors close to the color of the moving object, these methods may mistakenly track the objects in the background.
Object detecting and tracking methods based on pattern recognition learn characteristics of the object to be tracked in advance, and then detect the object from the images based on the characteristics learned in advance. These methods are effective for certain moving objects, such as pedestrians, vehicles, etc. However, the computation requirement of these methods is large, which places a high load on processing hardware.
The disclosed methods and systems address one or more of the problems listed above. For example, the disclosed methods and systems can accurately, quickly, and effectively detect a moving direction of an object from the images, and start tracking the moving object.